Estimation of Natural Gas Demand in Industry Sector of Iran: A Nonlinear Approach
Bibliographic record
Abstract
This paper attempt to estimate the natural gas demand function in Industry Sector of Iran for the period 1971 to 2009 using a regime-switching model entitled Smooth Transition Auto-regression model (STAR). To this end, explanatory variables such as value added of industry sector, real price of natural gas, real price of oil products, and real price of electricity are employed as variables influencing natural gas consumption in industry sector of Iran. The results show that natural gas demand in industry sector follows an LSTR1 model as a two-regime nonlinear model if real price of oil products is assumed as transition variable. The estimation results show that the slope parameter equals a high value of 10 and the threshold extreme value stands at 50.29 Rials per each liter of oil products consumed (Note 1). The results also indicate that in both regimes, value added of industry sector and real price of electricity have a positive and significant relation, and real price of natural gas has a reverse and significant relation with natural gas demand in industry sector. Further, real price of oil products does not have any significant relation with natural gas demand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".